提高效率，优化能源：流程工业制造中的数据驱动解决方案

Q3 Engineering

EAI Endorsed Transactions on Energy Web Pub Date : 2024-06-04 DOI:10.4108/ew.6098

Hui Liu, Guihao Zhang

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引用次数: 0

摘要

本文回顾了数据分析和机器学习技术的研究现状，重点关注其在流程工业制造中的应用，尤其是在控制和优化方面的应用。未来研究的关键领域包括用于过程监控的选择和迁移学习、处理时变特性以及利用特定领域的知识增强数据驱动的优化控制。此外，论文还探讨了强化学习技术和稳健优化，包括用于高层决策的分布式稳健优化。本文强调了工厂和流程历史知识的重要性，旨在找出知识差距，为流程工业数据驱动战略的未来研究铺平道路，尤其侧重于能源效率和优化。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Enhancing Efficiency and Energy Optimization: Data-Driven Solutions in Process Industrial Manufacturing

This paper reviews the current state of research in data analytics and machine learning techniques, focusing on their applications in process industrial manufacturing, particularly in control and optimization. Key areas for future research include selection and transfer learning for process monitoring, addressing time-varying characteristics, and enhancing data-driven optimal control with domain-specific knowledge. Additionally, the paper explores reinforcement learning techniques and robust optimization, including distributional robust optimization, for high-level decision-making. Emphasizing the importance of historical knowledge of plants and processes, this paper aims to identify knowledge gaps and pave the way for future research in data-driven strategies for process industries, with a particular emphasis on energy efficiency and optimization.

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来源期刊

EAI Endorsed Transactions on Energy Web Energy-Energy Engineering and Power Technology

CiteScore

2.60

自引率

0.00%

发文量

审稿时长

10 weeks

期刊介绍： With ICT pervading everyday objects and infrastructures, the ‘Future Internet’ is envisioned to undergo a radical transformation from how we know it today (a mere communication highway) into a vast hybrid network seamlessly integrating knowledge, people and machines into techno-social ecosystems whose behaviour transcends the boundaries of today’s engineering science. As the internet of things continues to grow, billions and trillions of data bytes need to be moved, stored and shared. The energy thus consumed and the climate impact of data centers are increasing dramatically, thereby becoming significant contributors to global warming and climate change. As reported recently, the combined electricity consumption of the world’s data centers has already exceeded that of some of the world''s top ten economies. In the ensuing process of integrating traditional and renewable energy, monitoring and managing various energy sources, and processing and transferring technological information through various channels, IT will undoubtedly play an ever-increasing and central role. Several technologies are currently racing to production to meet this challenge, from ‘smart dust’ to hybrid networks capable of controlling the emergence of dependable and reliable green and energy-efficient ecosystems – which we generically term the ‘energy web’ – calling for major paradigm shifts highly disruptive of the ways the energy sector functions today. The EAI Transactions on Energy Web are positioned at the forefront of these efforts and provide a forum for the most forward-looking, state-of-the-art research bringing together the cross section of IT and Energy communities. The journal will publish original works reporting on prominent advances that challenge traditional thinking.